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Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus
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Limiting the Number of Trees in Random Forests
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Wireless Communication Systems
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Ensemble Pruning: A Submodular Function Maximization Perspective
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Casting Random Forests as Artificial Neural Networks (and Profiting from It)
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Multi-view Regularized Gaussian Processes
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An Empirical Study of Building Compact Ensembles
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Simplified PAC-Bayesian Margin Bounds
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Influence of Hyperparameters on Random Forest Accuracy
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Incremental Learning by Heterogeneous Bagging Ensemble
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On Oblique Random Forests
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How Many Trees in a Random Forest?
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Diversity Regularized Ensemble Pruning
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Several Misconceptions and Misuses of Deep Neural Networks and Deep Learning
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Relations among m Sets of Measures
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An optimal pruning algorithm of classifier ensembles: dynamic programming approach
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A Survey on ensemble learning under the era of deep learning
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Privacy-preserving boosting
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An analysis of diversity measures
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Exact bootstrap k-nearest neighbor learners
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Ensemble clustering using semidefinite programming with applications
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On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms
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SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels
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Regression conformal prediction with random forests
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Learning a priori constrained weighted majority votes
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Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
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Adaptive random forests for evolving data stream classification
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Accelerated gradient boosting
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Boosting as a kernel-based method
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Sparse RBF Networks with Multi-kernels
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Bootstrap bias corrections for ensemble methods
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Regularizing axis-aligned ensembles via data rotations that favor simpler learners
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A survey on ensemble learning
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Graph-regularized multi-view semantic subspace learning
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Analysis of decision boundaries in linearly combined neural classifiers
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Standard errors for bagged and random forest estimators
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Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources
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Conflict management in Dempster–Shafer theory using the degree of falsity
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Classifier fusion in the Dempster–Shafer framework using optimized t-norm based combination rules
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Monte Carlo and quasi-Monte Carlo methods for Dempster's rule of combination
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Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning
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GFDC: A granule fusion density-based clustering with evidential reasoning
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Tabular data: Deep learning is not all you need
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Incremental construction of classifier and discriminant ensembles
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How to adjust an ensemble size in stream data mining?
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Logistic regression, neural networks and Dempster–Shafer theory: A new perspective
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Bagging and AdaBoost algorithms for vector quantization
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Efficient online recurrent connectionist learning with the ensemble Kalman filter
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Markowitz random forest: Weighting classification and regression trees with modern portfolio theory
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A learning algorithm for adaptive canonical correlation analysis of several data sets
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Boosting random subspace method
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Clustering: A neural network approach
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Multi-view clustering via multi-manifold regularized non-negative matrix factorization
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Error bounds for approximations with deep ReLU networks
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The capacity of feedforward neural networks
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MFC-ACL: Multi-view fusion clustering with attentive contrastive learning
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Linked independent component analysis for multimodal data fusion
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Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
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Information theoretic combination of pattern classifiers
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A novel framework for multi-class classification via ternary smooth support vector machine
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Auto-weighted multi-view clustering via deep matrix decomposition
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On selection and combination of weak learners in AdaBoost
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Online error correcting output codes
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Nonlinear canonical correlation analysis by neural networks
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Ensemble learning via negative correlation
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An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
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An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
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Improved Boosting Algorithms Using Confidence-rated Predictions
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MultiBoosting: A Technique for Combining Boosting and Wagging
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On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
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An Adaptive Version of the Boost by Majority Algorithm
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Boosting Methods for Regression
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Logistic Regression, AdaBoost and Bregman Distances
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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
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Robust Real-Time Face Detection
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Ensemble deep learning in bioinformatics
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Lower bounds for the rate of convergence of greedy algorithms
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Random Forest Prediction Intervals
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
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Targeted Random Projection for Prediction From High-Dimensional Features
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Bias-corrected random forests in regression
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A K -fold averaging cross-validation procedure
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Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate
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Canonical Analysis of Several Sets of Variables
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Reducing variability of crossvalidation for smoothing-parameter choice
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On ordered weighted averaging aggregation operators in multicriteria decisionmaking
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The combination of evidence in the transferable belief model
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The random subspace method for constructing decision forests
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On the algorithmic implementation of stochastic discrimination
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Learn++: an incremental learning algorithm for supervised neural networks
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Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
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Correlational spectral clustering
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Generalized Multiview Analysis: A discriminative latent space
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Going deeper with convolutions
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Diversity-induced Multi-view Subspace Clustering
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Temporal Multimodal Learning in Audiovisual Speech Recognition
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Multi-view Deep Network for Cross-View Classification
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
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Fast and Accurate Head Pose Estimation via Random Projection Forests
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
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Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
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Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
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Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data
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An incremental extremely random forest classifier for online learning and tracking
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Generalization error of ensemble estimators
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Scaling-up support vector machines using boosting algorithm
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On Ensemble Techniques for Data Stream Regression
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Fusion of Probability Density Functions
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Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]
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Improving Sparse Recovery on Structured Images with Bagged Clustering
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Deep-Sparse-Representation-Based Features for Speech Recognition
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Multiview Uncorrelated Discriminant Analysis
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WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory
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Generalized Multi-View Embedding for Visual Recognition and Cross-Modal Retrieval
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Multiple Kernel Fuzzy Clustering
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Efficient Kernel Orthonormalized PLS for Remote Sensing Applications
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Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
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Multimodal Similarity Gaussian Process Latent Variable Model
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Random Forests and Kernel Methods
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WildWood: A New Random Forest Algorithm
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K-Means-Based Consensus Clustering: A Unified View
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Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction
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Multi-View Concept Learning for Data Representation
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A Survey of Multi-View Representation Learning
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Multiple Kernel Clustering With Adaptive Multi-Scale Partition Selection
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Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes
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Mixing Linear SVMs for Nonlinear Classification
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Efficient Sparse Generalized Multiple Kernel Learning
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Incremental Learning of Concept Drift in Nonstationary Environments
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Soft Margin Multiple Kernel Learning
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Ensemble Pruning Using Spectral Coefficients
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Mining Recurring Concepts in a Dynamic Feature Space
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Generalized Multiple Kernel Learning With Data-Dependent Priors
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Partially Shared Latent Factor Learning With Multiview Data
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Alternative Multiview Maximum Entropy Discrimination
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An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition
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Incomplete Multisource Transfer Learning
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Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization
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Incomplete-Data Oriented Multiview Dimension Reduction via Sparse Low-Rank Representation
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Rescaled Boosting in Classification
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Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers
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Iterative Privileged Learning
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Marginalized Multiview Ensemble Clustering
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SVRG-MKL: A Fast and Scalable Multiple Kernel Learning Solution for Features Combination in Multi-Class Classification Problems
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Multiview Uncorrelated Locality Preserving Projection
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Why ResNet Works? Residuals Generalize
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GBDT-MO: Gradient-Boosted Decision Trees for Multiple Outputs
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Multiple Kernel k-Means Clustering by Selecting Representative Kernels
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Smoothness Regularized Multiview Subspace Clustering With Kernel Learning
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Multi-Source Contribution Learning for Domain Adaptation
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Multiple Kernel Clustering With Compressed Subspace Alignment
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Distributed Online Learning With Multiple Kernels
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Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions
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Online Extra Trees Regressor
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Inverse Adversarial Diversity Learning for Network Ensemble
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Regularized Simple Multiple Kernel k -Means With Kernel Average Alignment
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A Survey of Neural Trees: Co-Evolving Neural Networks and Decision Trees
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FloatBoost learning and statistical face detection
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A theoretical and experimental analysis of linear combiners for multiple classifier systems
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Combining multiple clusterings using evidence accumulation
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Clustering ensembles: models of consensus and weak partitions
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Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes
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Improving multiclass pattern recognition by the combination of two strategies
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Rotation Forest: A New Classifier Ensemble Method
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Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
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